In this tutorial, we explore how we can build a fully functional conversational AI agent from scratch using the Pipecat framework. We walk through setting up a Pipeline that links together custom FrameProcessor classes, one for handling user input and generating responses with a HuggingFace model, and another for formatting and displaying the conversation flow….
In the rapidly evolving field of agentic AI and AI Agents, staying informed is essential. Here’s a comprehensive, up-to-date list of the top blogs and websites—from industry leaders to academic voices—offering insights, tutorials, and reviews focused on AI agents and Agentic AI in 2025. 1. OpenAI Blog The official blog of OpenAI, creators of landmark…
The landscape of software engineering automation is evolving rapidly, driven by advances in Large Language Models (LLMs). However, most approaches to training capable agents rely on proprietary models or costly teacher-based methods, leaving open-weight LLMs with limited capabilities in real-world scenarios. A team of researchers from Nebius AI and Humanoid introduced a reinforcement learning framework…
In this tutorial, we walk through building a compact but fully functional Cipher-based workflow. We start by securely capturing our Gemini API key in the Colab UI without exposing it in code. We then implement a dynamic LLM selection function that can automatically switch between OpenAI, Gemini, or Anthropic based on which API key is…
China continues to set the pace in open-source large-language-model innovation, especially for agentic architectures and deep reasoning. Here is a comprehensive, up-to-date guide to the best Chinese open agentic/reasoning models, expanded with the newest and most influential entrants. 1. Kimi K2 (Moonshot AI) Profile: Mixture-of-Experts architecture, up to 128K context, superior agentic ability and bilingual…
RouteLLM is a flexible framework for serving and evaluating LLM routers, designed to maximize performance while minimizing cost. Key features: Seamless integration — Acts as a drop-in replacement for the OpenAI client or runs as an OpenAI-compatible server, intelligently routing simpler queries to cheaper models. Pre-trained routers out of the box — Proven to cut…
The year 2025 marks a defining moment in the evolution of artificial intelligence, ushering in an era where agentic systems—autonomous AI agents capable of complex reasoning and coordinated action—are transforming enterprise workflows, research, software development, and day-to-day user experiences. This articles focuses on five core AI agent trends for 2025: Agentic RAG, Voice Agents, AI…
Table of contents Why Classic AI Agent Workflows Fail The 9 Agentic Workflow Patterns for 2025 Sequential Intelligence Parallel Processing Intelligent Routing Self-Improving Systems How These Patterns Revolutionize AI Agents Real-World Impact & Implementation Best Practices Conclusion AI agents are at a pivotal moment: simply calling a language model is no longer enough for production-ready…
In this tutorial, we walk through building an advanced PaperQA2 AI Agent powered by Google’s Gemini model, designed specifically for scientific literature analysis. We set up the environment in Google Colab/Notebook, configure the Gemini API, and integrate it seamlessly with PaperQA2 to process and query multiple research papers. By the end of the setup, we…
The Mixture-of-Agents (MoA) architecture is a transformative approach for enhancing large language model (LLM) performance, especially on complex, open-ended tasks where a single model can struggle with accuracy, reasoning, or domain specificity. How the Mixture-of-Agents Architecture Works Layered Structure: MoA frameworks organize multiple specialized LLM agents in layers. Each agent within a layer receives all…